One of the most important concepts of recent information technology is collecting data in a central repository from a number of different data sources (external data sources such as: Open data, Financial data, Social Media, Geo data, public sensors, etc.; internal data sources: Corporate portals, sensors—internet of things (IOT), on-premise systems for CRM, accounting, etc.) and analyzing the collected data much faster than was possible even a couple of years ago. The basic idea is that with all the processing power presently available, and a little creativity, researchers should be able to find novel patterns and relationships among different kinds of information.
Over the last few years this sort of analysis was generally referred to as “Big Data.” Now Big Data is evolving, becoming more “hyper” and including all sorts and varieties of data sources. This “hyperdata” is a prerequisite for high level contextual services based on cognitive computing (e.g. IBM Watson®) or any other kind of data-driven application. The goal is to provide services that are founded on knowledge of a user's context to deliver personalized services based on real-time and historical data. Contextual technology can create a personalized user experience that can anticipate needs and provide intelligent recommendations and predictions.
As discussed above, hyperdata includes various forms of data from a number of sources. For example, social media serves as a fount of highly personalized content and relevant news. Almost every electronic device generates data and for decades, only a sliver of this information has been captured. Sensors in technology are becoming more important and ubiquitous, and generate huge amounts of business-relevant real-time data. Further, location-based services add an important dimension to a user's context.
Currently all applications that deal with a combination of external and internal data sources have to take care of the correct extraction of data, building a common data model and exposing the data by appropriate services. What is needed is a way to “harvest” business-relevant data from a number of various sources, and to build a hyperdata hub to access an enriched data model which exposes the interrelated hyperdata, to finally harness a vast quantity of information and to create a personalized and intelligent user experience.